Using these two complexity measures, Novak et al. [1]} study the correlation between complexity and generalization. They demonstrate that neural networks have strong robustness in the vicinity of the training data manifold where deep models have good generalization capability. They also show that the factors associated with poor generalization (e.g., full-batch training, random labels) correspond to weaker robustness, and the factors associated with good generalization (e.g., data augmentation, ReLU) correspond to stronger robustness.
